fitness.c

基于遗传算法的无功优化matlab实现方法软件包

/* file : fitness.c
 */

#include <stdio.h>
#include <stdlib.h>

void
fitness(popu,FIT,p_size,len,pat,tar,n,h,p,k,m,te,fileName,
		resultName,critio,total,gen)
int	**popu;	/* pointer to the population */
double	*FIT;	/* pointer to the fitness vector */
int		p_size;	/* population size */
int 		len;	/* length of individual vector */
double 	**pat;		/* input patterns */
double 	**tar;	/* target */
int 	n;	/* input dim */
int 	h;  /* hidden dim */
int 	p; /* output dim */
int	k;  /* each para represented by k bits */
int	m;	  /* training patterns number */
int	*te;	/* flag for termination */
char	*fileName; /* weightFile */
char	*resultName;	/* resultFile Name */
double 	critio;	/* minimum mean square */
int gen;	/* generation */
{
	int i;
	int ind,max;		/* index for maximum */
	int sa;	/* save best weights */
	
	double	**weight_ih,**weight_ho;

	extern void getWeight(int *,int,int,int,int,int,double **,double **);
	extern double bp(double **,double **,double **,double **,
					  int, int, int,int,int *,char *,char *,double,int,int);
	extern int maximum(double *,int);

	weight_ih=(double **)calloc(h,sizeof(double *));
	for (i=0;i<h;i++)
		weight_ih[i]=(double *)calloc((n+1),sizeof(double));

	weight_ho=(double **)calloc(p,sizeof(double *));
	for (i=0;i<p;i++)
		weight_ho[i]=(double *)calloc((h+1),sizeof(double));

	ind =-1;
	if ((gen%100)==1)
		ind=maximum(FIT,p_size);

	/* fitness calculation */
	for (i=0;i<p_size;i++)
	{
		/* convert binary vector to weight coefficients */
		getWeight(popu[i],len,k,n,h,p,weight_ih,weight_ho);

		/* bp, get fitness  */
		sa=0;
		if (i==ind)
			sa=1;

		FIT[i]=bp(weight_ih,weight_ho,pat,tar,n,h,p,m,te,
				fileName,resultName,critio,total,sa);

	}

	ind=maximum(FIT,p_size);
	max=FIT[ind];
	if (max<=1.0)
		max=1.0;

	/* calculate final fitness */
	for (i=0;i<p_size;i++)
		FIT[i]=max-FIT[i]+0.00001;


	for (i=0;i<h;i++)
		free(weight_ih[i]);
	free(weight_ih);
	for (i=0;i<p;i++)
		free(weight_ho[i]);
	free(weight_ho);
}